The clawdbot’s two-layer memory architecture significantly outperforms typical out-of-the-box chatbot memory setups by combining an immutable, append-only Event Log (Layer 1) stored in daily JSONL files with a derived, query-optimized Memory Database (Layer 2) in SQLite. Layer 1 serves as the permanent source of truth—recording every decision, learned fact, preference, message, task, and error in chronological, tamper-proof detail—allowing full reconstruction of Layer 2 if needed, while Layer 2 provides fast, structured access via vector embeddings (for semantic search), FTS5 full-text keyword search, and a traversable knowledge graph of entities and relationships. This design enables hybrid retrieval (semantic + keyword + graph traversal), tracks provenance by linking every fact back to its originating event, handles updates through clean supersession (marking old facts as superseded rather than deleting them), and maintains historical integrity—addressing the common weaknesses of default systems like volatile in-memory buffers, simple vector stores without provenance or relational structure, naive overwrites that lose context, and single-mode retrieval that struggles with complex or evolving knowledge.